Application Deadline – 17th December 2022
4 year funded PhD studentship is available in a partnership between the UCL Department of Medical Physics and Biomedical Engineering, the Dimensional Metrology Group at the National Physical Laboratory NPL and Nikon X-Tek System Ltd.
X-rays are unique for investigating bulky and opaque samples, as their broad use in medicine, security and non-destructive inspection demonstrates. X-ray phase contrast imaging (XPCI) complements the capabilities of conventional radiography, especially for low-absorbing samples. Biological soft matter does not absorb X-rays with high probability, but the capability of detecting phase distortions in the X-ray wavefront enables visualising details that would be otherwise X-ray-invisible. The Advanced X-ray Imaging Group (AXIm) has pioneered the development of compact XPCI systems, and a partnership with Nikon has built a first prototype, field deployed for intraoperative imaging of breast tissue. The image quality was vastly superior to the current standards, however the interpretation of images remained largely qualitative and highly subject-dependent. Calibration and uncertainty evaluation are fundamental elements of instrument traceability and NPL is leading the development of X-ray CT for dimensional metrology. The creation of a metrology framework for XPCI micro-CT applied in the medical field would be a step change in the efforts to transform three-dimensional images into absolute measurements, where the instrument's bias, precision, and accuracy are known and characterised.
The successful candidate will develop new techniques, with unique sensitivity for soft-tissues and other low-Z, with a quantitative and traceable methodology. These new approaches will provide a transformative pathway for XCT across applications, encompassing three-dimensional imaging of tissue, design, manufacturing and pre-clinical small-animal investigations.
For further details regarding this PhD studentship can be found here:
https://www.ucl.ac.uk/intelligent-imaging-healthcare/case-studies/2022/nov/quantitative-x-ray-phase-contrast-imaging
|